VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Selecting Bankruptcy Predictors Using a Support Vector Machine Approach
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Time-line based model for software project scheduling with genetic algorithms
Information and Software Technology
Expert system based on artificial neural networks for content-based image retrieval
Expert Systems with Applications: An International Journal
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
A data clustering algorithm for stratified data partitioning in artificial neural network
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this study, we proposed a quick and accurate algorithm for content-based image classification. The proposed method is also used to retrieve similar images from databases. In this paper color and texture information are used to represent image features. The basic idea is to extract color information about global and local features of images. A global color feature is extracted by an RGB model. While, a local color feature is extracted by an HSV model. In the case of a local feature, if it cannot be classified, the result is inaccurate retrieval. A GA (genetic algorithm) is used to extract local features which can be classified. Local features extracted by a GA are optimal representative features. In the experiment, the accuracy of image classification is measured using the proposed algorithm. Also, we compared the previous algorithm with the proposed algorithm in terms of image classification performance. As a result, the proposed algorithm showed higher performance in terms of accuracy.